通过选择性过滤缓解误导性思维链推理
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering
AI总结：
针对思维链推理质量不可靠的问题，本文提出SelF-Reasoner，通过评估问题与推理链的蕴含关系选择性使用CoT，在多个任务上稳定超越T5基线。
摘要：
大型语言模型通过利用思维链（CoT）推理技术，以逐步推理链解决复杂问题，展现出卓越的能力。尽管取得了成功，这种推理的有效性本质上取决于CoT的质量。然而，由于存在不可分解的问题以及可能出现的错误推理链，尤其是在小规模语言模型的情况下，无法保证完美的CoT推理。为应对这一挑战，我们提出了一种新颖的方法，称为选择性过滤推理器（SelF-Reasoner），该方法评估问题与候选推理链之间的蕴含关系。然后，当推理链表现出置信度时，我们继续进行CoT推理；否则，我们选择直接预测答案。SelF-Reasoner在ScienceQA、ECQA和LastLetter任务上一致地改进了微调后的T5基线。代码可在\ exttt{https://github.com/LibroWu/SelF-Reasoner}获取。
英文摘要：
Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning chains. Despite its success, the efficacy of such reasoning is inherently contingent upon the quality of CoT. However, flawless CoT reasoning cannot be guaranteed due to the presence of indecomposable questions and the potential for erroneous reasoning chains, particularly in the case of small-scale language models. To tackle this challenge, we propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. Then, we proceed with CoT reasoning when the reasoning chain demonstrates confidence; otherwise, we opt to predict the answer directly. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the ScienceQA, ECQA, and LastLetter tasks. Code is available at \texttt{https://github.com/LibroWu/SelF-Reasoner}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
